Simulation engines are widely adopted in robotics. However, they lack either full simulation control, ROS integration, realistic physics, or photorealism. Recently, synthetic data generation and realistic rendering has advanced tasks like target tracking and human pose estimation. However, when focusing on vision applications, there is usually a lack of information like sensor measurements or time continuity. On the other hand, simulations for most robotics tasks are performed in (semi)static environments, with specific sensors and low visual fidelity. To solve this, we introduced in our previous work a fully customizable framework for generating realistic animated dynamic environments (GRADE) [1]. We use GRADE to generate an indoor dynamic environment dataset and then compare multiple SLAM algorithms on different sequences. By doing that, we show how current research over-relies on known benchmarks, failing to generalize. Our tests with refined YOLO and Mask R-CNN models provide further evidence that additional research in dynamic SLAM is necessary. The code, results, and generated data are provided as open-source at https://eliabntt.github.io/grade-rrSimulation of Dynamic Environments for SLAM

翻译：仿真引擎在机器人领域中被广泛采用。然而，它们往往无法同时满足完全仿真控制、ROS集成、逼真物理效果或照片级渲染的要求。近年来，合成数据生成与真实感渲染技术推动了目标跟踪和人体姿态估计等任务的发展。但在视觉应用中，传感器测量数据或时间连续性等信息通常较为缺乏。另一方面，大多数机器人任务的仿真是在（半）静态环境中进行的，仅使用特定传感器且视觉保真度较低。为解决这一问题，我们在此前的工作中提出了一种用于生成逼真动态环境的全定制化框架GRADE [1]。我们利用GRADE生成了一个室内动态环境数据集，并在不同序列上对多种SLAM算法进行了比较。通过这一研究，我们揭示了当前研究过度依赖已知基准而缺乏泛化能力的问题。我们使用改进的YOLO和Mask R-CNN模型进行的测试进一步证明，动态SLAM领域的额外研究是必要的。相关代码、实验结果及生成的数据已作为开源资源发布在https://eliabntt.github.io/grade-rr